HR analytics

1. HR Analytics
Module -1

2. Module-1
• Introduction to HR Analytics:
• Evolution of Business Analytics,
• Motivation for Studying Business Analytics,
• Emergence of Business Analytics,
• Understanding Business Analytics,
• Managing a Business Analytics Project,
• Advantages of Business Analytics,
• Making the Best Use of Business Analytics,
• Challenges to Business Analysts,
• Analytics in Different Domains of Business,
• Levels of Analytics Maturity.

3. Introduction to HR Analytics
Definition: Human Resource analytics (HR
Analytics) is defined as the area in the field
of analytics that deals with people analysis
and applying analytical process to the
human capital within the organization to
improve employee performance and
improving employee retention.

4. HR analytics doesn’t collect data about how your employees
are performing at work, instead, its sole aim is to provide better
insight into each of the human resource processes, gathering
related data and then using this data to make informed
decisions on how to improve these processes.
For example, using HR analytics you can answer the following
questions about the organization’s HR system:
1. How high is your employee turnover rate?
2. Do you know which of your employees will leave your
organization within a year?
3. What percentage of employee turnover is regretted loss?

5. Most human resource professionals will be easily able to
answer the first question for their organization.
However, answering the other two questions will be tricky,
especially if you don’t have a detailed data for it.
In order to answer the other two questions, as a professional,
you would need to combine different data and analyze it
thoroughly. Human resources tend to collect a good amount
of data but are unaware of how to use this data. Well, here is
the answer! Use it now to analyze your human capital and
make informed decisions. As soon as an organization starts
to analyze their people problems using the collected data,
they are engaged in active HR analytics.

6. Business Analytics
Introduction
The word analytics has come into the foreground in last decade or so. The
proliferation of the internet and information technology has made analytics very
relevant in the current age. Analytics is a field which combines data, information
technology, statistical analysis, quantitative methods and computer-based models into
one. This all are combined to provide decision makers all the possible scenarios to
make a well thought and researched decision. The computer-based model ensures
that decision makers are able to see performance of decision under various scenarios.
“Business analytics is the process of collating, sorting, processing, and studying
business data, and using statistical models and iterative methodologies to transform
data into business insights. The goal of business analytics is to determine which
datasets are useful and how they can be leveraged to solve problems and increase
efficiency, productivity, and revenue.”
Application
Business analytics has a wide range of application from customer relationship
management, financial management, and marketing, supply-chain
management, human-resource management, pricing and even in sports through team
game strategies.

7. Importance of Business Analytics
• Business analytics is a methodology or tool to make a sound commercial
decision. Hence it impacts functioning of the whole organization. Therefore,
business analytics can help improve profitability of the business, increase
market share and revenue and provide better return to a shareholder.
• Facilitates better understanding of available primary and secondary data,
which again affect operational efficiency of several departments.
• Provides a competitive advantage to companies. In this digital age flow of
information is almost equal to all the players. It is how this information is
utilized makes the company competitive. Business analytics combines
available data with various well thought models to improve business
decisions.
• Converts available data into valuable information. This information can be
presented in any required format, comfortable to the decision maker.

8. Components of Business Analytics
• Data Aggregation: Before data can be analyzed, it must be collected, centralized, and cleaned to
avoid duplication, and filtered to remove inaccurate, incomplete, and unusable data. Data can be
aggregated from:
– Transactional records: Records that are part of a large dataset shared by an organization or by an authorized
third party (banking records, sales records, and shipping records).
– Volunteered data: Data supplied via a paper or digital form that is shared by the consumer directly or by an
authorized third party (usually personal information).
• Data Mining: In the search to reveal and identify previously unrecognized trends and patterns,
models can be created by mining through vast amounts of data. Data mining employs several
statistical techniques to achieve clarification, including:
– Classification: Used when variables such as demographics are known and can be used to sort and group data
– Regression: A function used to predict continuous numeric values, based on extrapolating historical patterns
– Clustering: Used when factors used to classify data are unavailable, meaning patterns must be identified to
determine what variables exist
• Association and Sequence Identification In many cases, consumers perform similar actions at the
same time or perform predictable actions sequentially. This data can reveal patterns such as:
– Association: For example, two different items frequently being purchased in the same transaction, such as
multiple books in a series or a toothbrush and toothpaste.
– Sequencing: For example, a consumer requesting a credit report followed by asking for a loan or booking an
airline ticket, followed by booking a hotel room or reserving a car.
• Text Mining: Companies can also collect textual information from social media sites, blog
comments, and call center scripts to extract meaningful relationship indicators. This data can be
used to:
– Develop in-demand new products
– Improve customer service and experience
– Review competitor performance

9. • Forecasting: A forecast of future events or behaviors based on historical data can be created by
analyzing processes that occur during a specific period or season. For example:
– Energy demands for a city with a static population in any given month or quarter
– Retail sales for holiday merchandise, including biggest sales days for both physical and digital stores
– Spikes in internet searches related to a specific recurring event, such as the Super Bowl or the Olympics
• Predictive Analytics: Companies can create, deploy, and manage predictive scoring models,
proactively addressing events such as:
– Customer churn with specificity narrowed down to customer age bracket, income level, lifetime of existing
account, and availability of promotions
– Equipment failure, especially in anticipated times of heavy use or if subject to extraordinary
temperature/humidity-related stressors
– Market trends including those taking place entirely online, as well as patterns which may be seasonal or
event-related
• Optimization: Companies can identify best-case scenarios and next best actions by developing and
engaging simulation techniques, including:
– Peak sales pricing and using demand spikes to scale production and maintain a steady revenue flow
– Inventory stocking and shipping options that optimize delivery schedules and customer satisfaction without
sacrificing warehouse space
– Prime opportunity windows for sales, promotions, new products, and spin-offs to maximize profits and pave
the way for future opportunities
• Data Visualization: Information and insights drawn from data can be presented with highly
interactive graphics to show:
– Exploratory data analysis
– Modeling output
– Statistical predictions
These data visualization components allow organizations to leverage their data to inform and drive new
goals for the business, increase revenues, and improve consumer relations.

10. Emergence of Business Analytics
Business analytics has been existence since very long time and
has evolved with availability of newer and better technologies.
It has its roots in operations research, which was extensively
used during World War II. Operations research was an
analytical way to look at data to conduct military operations.
Over a period of time, this technique started getting utilized
for business. Here operation’s research evolved into
management science. Again, basis for management science
remained same as operation research in data, decision making
models, etc.
As the economies started developing and companies became
more and more competitive, management science evolved
into business intelligence, decision support systems and into
PC software.

11. Evolution of Business Analytics
“Business analytics refers to the skills, technologies, and practices for
continuous iterative exploration and investigation of past business
performance to gain insight and drive business planning.”
Covering all areas of business transactions, industry, and verticals, business
analytics measure daily insights in the areas of…
• Finance
• Sales
• Marketing
• Social media
• Search engine optimization (SEO)
• Consumer data
• Target audience data
• E Commerce
• Human resources
• and much, much more…

13. Business Analytics in a Barter Economy
Let’s take a moment and go way back if only for the sake of giving ourselves a baseline example.
Imagine how one might have tracked the first barter system without access to pencils and paper.
Numerical markings on cave-dwelling walls or with wood and stones are our first glimpse at the
evolution of business analytics. A tracking system of sorts would have been needed to trace who
had what and when.
This is an obvious oversimplification, but from this example, we can better understand how and
why business analytics evolved as industry expanded.
Business Analytics in the Industrial Era
The industrial revolution which began in the mid- to late-1700s brought with it new
manufacturing processes with water and steam followed soon after by railroads, steel, and oil.
These are complex industries that quickly grew out of their local storefronts into nation-wide
companies.
During the late-1800s Frederick W. Taylor introduced the first formalized system of business
analytics in the United States. Taylor’s System of Scientific Management began with time studies
that analyzed production techniques and laborer’s body movements to find greater efficiencies
that ultimately boosted industrial production. Taylor acted as a consultant to Henry Ford and
directly influenced Ford’s car assembly line time measurements.
In the early 1900s, Ford measured the time each component of his Ford Model T took to develop
through completion on his assembly line. Perhaps a seemingly simple task, but Ford
singlehandedly revolutionized not only the automobile industry, but manufacturing world-wide.
It’s safe to say that the earlier days in the evolution business analytics focused mostly on
improving production; its efficiencies, quantities, and cost-effectiveness.

14. Operational Reporting
Operational reporting still functions in most of today’s businesses as a day-to-day summary of what’s
happening now. But leading up to the digital and informational ages in the late 1900s, operational
reporting rued the day as highly segmented workflow analytics.
This means information was gathered and saved, but typically housed in informational silos that weren’t
easily shared company-wide. It’s not that this was anyone’s specific intention, but it was a tremendous
challenge to update and share, for example, a handwritten ledger that analyzed the company’s daily
reports. Operational reporting resulted in very little integration and low to zero historical data.
Organizationally speaking, the challenge of sharing information was great. The bigger the business, the
more challenging the data collection process.
Business Analytics in the Digital & Information Ages
Behold the 1970s when computers began to be in regular use at larger corporations. Business analytics
in this era were headed by Decision Support Systems (DSS). DSSs grew in popularity as they helped to
sort and filter larger quantities of data that assisted executives in data-driven business decision-making.
DSS systems helped collate data from various areas of business, for example production and sales, to
give key decision-makers a bird’s eye perspective of business in a way that hadn’t really existed before.
Examining different slices of data through filtering processes was a game-changing experience in the
world of business innovation.
DSS analytics tools are typically driven by the following process:
Automated Inputs → User Inputs → Outputs → Results
Computers continued to boom throughout the 1980s and into the 1990s (and certainly beyond) in
what’s commonly called the Information Age. A big part of this information was initially historical
information. With the technology boom of the Information Age comes a tremendous increase in
information storage capacity.
Suddenly, data warehouses could save historical computer data (market trends, growth, pricing)
gathered over time and prepped for data analysis. Earnings and operations reports became a regularly
accepted way to understand businesses and began to fuel business dealings, investments, and decision-
making.

15. Meet Microsoft Excel
Microsoft Excel built upon this DDS-type platform and introduced its still increasingly popular software
in 1985. Excel enables its users to not only sort and filter data, but to program formulas that splice and
display the data as specifically instructed. Long gone were the days of handwritten ledgers once
Microsoft Excel spreadsheets entered the scene.
Meet Google Analytics
Google Analytics were introduced in 2005. The world was already being overrun by data available since
the dawn of the Information Age and finally Google provided a free way for its users to begin to analyze
at least some of it. While hardly perfect and arguably not so user-friendly, when considering the
evolution of business analytics, Google Analytics is a far cry from the early days of Taylor and Ford’s time
and efficiency studies.
Google Analytics allows its users to dive deeper into very specific metrics Taylor and Ford might only
have dreamed of knowing. Digitally focused, Google Analytics enables website owners who install a
specific line of code into their sites to discover metrics like…
• Audience demographics
• New vs. returning users
• Device type
• Time spent on site
• Bounce rate
• Digital advertising data
• Total visits, views, click-throughs, and more
Google Analytics is hardly a catch-all platform, but its role in the evolution of business analytics is
tremendous as it was one of the first of its kind to deliver immediate accessibility in every household
and business that has a computer and website.

16. The Future of Business Analytics
Real-time analytics: Real-time analytics are data collected and reported on in-the-
moment, or in real-time. An example of this might be that an ecommerce store owner
could witness a sale coming through the owner’s website as it happens.
Big data: With huge chunks of historical data available in conjunction with real-time
cloud data drawn from a tremendous user base, big data is groundbreaking in its
ability to move the evolution of business analytics forward.
Predictive analytics: Based on past trends, predictive analytics looks to big data
collected over time to predict future actions.
Automated analytics: Automated analytics are analytics that ultimately require very
few to zero manual inputs. Data is automatically analyzed in ways that optimize
business systems.
The evolution of business analytics will continue to evolve as it has done so
throughout the ages. Perhaps what we currently deem the future of business analytics
will one day soon be as obsolete as tracking sales with sticks and stones, but in the
meantime, let’s agree to appreciate the technology we have and use it to make the
best possible business decisions we can.

17. Scope of Business Analytics
Business analytics has a wide range of application and usages.
• It can be used for descriptive analysis in which data is utilized to
understand past and present situation. This kind of descriptive
analysis is used to asses’ current market position of the company
and effectiveness of previous business decision.
• It is used for predictive analysis, which is typical used to asses’
previous business performance.
• Business analytics is also used for prescriptive analysis, which is
utilized to formulate optimization techniques for stronger business
performance.
• For example, business analytics is used to determine pricing of
various products in a departmental store based past and present set
of information

18. Advantages of Business Analytics
5 Benefits of Using Business Analytics
• Analytics helps you measure how much of your
mission statement is accomplished.
• Analytics Encourages Smart Decision-Making.
• Analytics Provides Clearer Insights Through Data
Visualization.
• Analytics Keep You Updated.
• Analytics Offer Efficiency.

19. Challenges Presented by Business Analytics
• The amount of data being collected
• Collecting meaningful and real-time data
• Visual representation of data
• Data from multiple sources
• Inaccessible data
• Poor quality data
• Pressure from the top
• Lack of support
• Confusion or anxiety
• Budget
• Shortage of skills

20. Analytics in Different Domains of Business
Top domains that Big Data analytics helps to transform
• Financial services: Due to quantitative nature, Financial Services and Fin tech are a perfect fit
for Data Science, Machine Learning, and Big Data analytics. There are many open-source
machine learning algorithms and tools that are compatible with financial data and help to
produce actionable and accurate insights. Also, renowned financial services companies have
deep pockets and can afford to spend a lot on state-of-the-art computing equipment.
• Security and financial monitoring: For example, banks and financial institutions can leverage
this technology to monitor thousands of transaction parameters for every account in real
time. The algorithm assesses each action a cardholder takes and defines if an attempted
activity is characteristic of that particular user. Such model flags signs of fraudulent behavior
quite accurately.
• Credit scoring: Banks and insurance companies have a vast amount of historical consumer
data, so they can apply Data Science models to these big datasets. Also, they can use data
generated by large telecom or utility companies. As a result, if a person has a thin credit
history, they can obtain the payment probability score.

21. • E-commerce: By analyzing logs and previous customer behavior e-commerce businesses can
achieve more targeted and personalized marketing and thus boost sales and performance.
Another important aspect is managing stocks and predicting products in demand. It helps to
keep track of stock levels, determine costs for shipping and storing, identify a storage
location, and predict future demand. This way you can make sure you have enough in-
demand products to sell to your customers, and thus increase customer satisfaction and
product sales. Also, it helps to predict and allocate costs.
• Aviation: Big Data Analytics is especially important in industries like aviation as there is no
physical access to the testing environment. Tech hitches can’t be fixed by the cabin crew on
the go and only in the biggest airports have maintenance teams. Thus, Big Data analytics
solutions can predict the probability of a plane being delayed or cancelled due to technical
hitches. Another important aspect is predicting aircraft engine parts failures as aircraft engine
part replacements are among the most common and critical maintenance tasks in the
aviation industry.
• Transportation and logistics: Data Science and Big Data analytics are real game-changers for
the transportation industry. They help to optimize routing and freight movement.
Additionally, they help with fleet optimization and predictive maintenance through the real-
time view of fleet operating conditions. Also, they help to optimize transit schedules by
predicting the impact of maintenance, road-works, congestion and accidents.

22. Analytics Maturity
• Analytics Maturity is a model for assessing an
organizations ability to effectively practice
data exploration and decision-making using
levels or stages. The Analytics Maturity model
can be easily broken down into 5 simple
segments by using “Analytics Maturity Curve”.

24. • Descriptive – What Happened?: The beginning. The Descriptive Analytics
phase asks the question “What Happened”, by performing operational
reporting (which is often done manually and heavily Excel driven), data
exploration, and benchmarking. Most organizations are at this stage of
development, or reactive within the analytics maturity model. It is largely
centered around reporting to lay the groundwork and develop a single
source of truth.
• Diagnostic – Why Did it Happen? : Very similar to the Descriptive
Analytics but adding in a desire to answer the questions, “why did it
happen, why is it happening”. It is taking data exploration a step further to
analyze historical and past data to produce insights about the present. For
example, some of the insights that organizations rely on are market
segmentation, using social media to understand customer satisfaction
issues, IT backlogs, etc.
• Predictive – What Will Happen?: Predictive Analytics answers the
question of, “What will happen?” by utilizing statistical analysis, predictive
models, forecasting and scenario planning. These analytics tools provide
a better understanding of future scenarios and the implications to your
business.

25. • Prescriptive – What Should We Do About It?: We’ve already answered the what, why and
what will happen. So now the next important question is, “What should we do about it?”
Prescriptive Analytics takes Descriptive and Predictive a step further by improving the
accuracy of our predictions and continually processing and automating new data, in turn fully
optimizing decision making.
• Cognitive – What I Don’t Know?: Cognitive Analytics is the highest level of automation.
Involves machine learning and natural language processing. It answers the question, “What
insights don’t I know about yet?” Even though this is the top tier of analytics maturity, the
practice of Cognitive Analytics can be used in the prior levels. Jean Francois Puget says it best
in an IBM blog post, in which he states, “Cognitive computing is not another level of
analytics, it rather extends the analytics journey to areas that were unreachable with more
classical analytics techniques like business intelligence, statistics, and operations research.”
• Conclusion: Here is the bottom line: with the overwhelming amount of data available today,
organizations need analytics more than ever. Given these points, the Analytics Maturity
Model is the easiest, most comprehensive way to track and determine the development of
analytics within your organization.